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Automatic Calibration Framework of Agent-Based Models for Dynamic and Heterogeneous Parameters

Agent-based models (ABMs) highlight the importance of simulation validation, such as qualitative face validation and quantitative empirical validation. In particular, we focused on quantitative validation by adjusting simulation input parameters of the ABM. This study introduces an automatic calibration framework that combines the suggested dynamic and heterogeneous calibration methods. Specifically, the dynamic calibration fits the simulation results to the real-world data by automatically capturing suitable simulation time to adjust the simulation parameters. Meanwhile, the heterogeneous calibration reduces the distributional discrepancy between individuals in the simulation and the real world by adjusting agent related parameters cluster-wisely.

preprint2022arXivOpen access
Dongjun KimTae-Sub YunIl-Chul MoonJang Won Bae
Artificial IntelligenceMultiagent Systems
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Open access4 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
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Dongjun KimTae-Sub YunIl-Chul MoonJang Won Bae

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